Update app.py

app.py

@@ -116,13 +116,53 @@ def transcribe_audio(audio_file):
         print(f"Transcription error details: {str(e)}")
         raise Exception(f"Transcription error: {str(e)}")
 
+def analyze_emotion(text):
+    """Analyze text emotion using Hugging Face API"""
+    API_URL = "https://api-inference.huggingface.co/models/SamLowe/roberta-base-go_emotions"
+    headers = {"Authorization": f"Bearer {os.getenv('HUGGINGFACE_API_KEY')}"}
+    
+    try:
+        response = requests.post(API_URL, headers=headers, json={"inputs": text})
+        emotions = response.json()
+        
+        # Extract emotion scores
+        if isinstance(emotions, list) and len(emotions) > 0:
+            emotion_scores = emotions[0]
+            
+            # Define urgent emotions
+            urgent_emotions = ['anger', 'fear', 'annoyance', 'disapproval', 'nervousness']
+            high_priority_emotions = ['desire', 'excitement', 'surprise']
+            
+            # Calculate urgency based on emotional content
+            urgent_score = sum(score for emotion, score in emotion_scores.items() 
+                             if emotion in urgent_emotions)
+            high_priority_score = sum(score for emotion, score in emotion_scores.items() 
+                                    if emotion in high_priority_emotions)
+            
+            # Determine urgency level based on emotion scores
+            if urgent_score > 0.5:
+                return "urgent"
+            elif high_priority_score > 0.4 or urgent_score > 0.3:
+                return "high"
+            return "normal"
+            
+        return "normal"
+    except Exception as e:
+        print(f"Error in emotion analysis: {str(e)}")
+        return "normal"
+
 def improve_task_description(text):
-    """Improve and summarize task description using SambaNova API"""
+    """Improve and summarize task description using SambaNova API and emotion analysis"""
     try:
+        # First analyze emotion to get initial urgency assessment
+        emotion_urgency = analyze_emotion(text)
+        
         prompt = f"""Please analyze and structure this task description, including determining its urgency level. 
 
 Original task: {text}
 
+Initial emotion-based urgency assessment: {emotion_urgency}
+
 Please provide:
 1. A clear, concise task title
 2. Key objectives
@@ -133,8 +173,10 @@ Please provide:
    - Deadlines mentioned
    - Impact and consequences described
    - Business criticality
+   - Emotional context and tone
    
 Format the response with "URGENCY_LEVEL: [level]" as the first line, followed by the structured description.
+Consider the emotion-based urgency assessment provided above when making the final urgency determination.
 """
 
         headers = {
@@ -167,12 +209,17 @@ Format the response with "URGENCY_LEVEL: [level]" as the first line, followed by
         # Extract urgency level and description
         lines = response_text.split('\n')
         urgency_line = lines[0].strip()
-        urgency = "normal"  # default
+        
+        # Use emotion-based urgency as fallback
+        urgency = emotion_urgency
         
         if urgency_line.startswith("URGENCY_LEVEL:"):
             level = urgency_line.split(":")[1].strip().lower()
             if level in ["normal", "high", "urgent"]:
-                urgency = level
+                # Compare with emotion-based urgency and use the higher priority
+                urgency_levels = {"normal": 0, "high": 1, "urgent": 2}
+                if urgency_levels[level] > urgency_levels[emotion_urgency]:
+                    urgency = level
             description = '\n'.join(lines[1:]).strip()
         else:
             description = response_text